Multimodal analysis of personality traits on videos of self-presentation and induced behavior

Giritlioğlu, Dersu; Mandira, Burak; Yılmaz, Selim; Ertenli, Can Ufuk; Akgür, Berhan Faruk; Kınıklıoğlu, Merve; Kurt, Aslı Gül; Mutlu, Emre; Gürel, Şeref Can; Dibeklioğlu, Hamdi

doi:10.1007/s12193-020-00347-7

Cited by 18 publications

(7 citation statements)

References 77 publications

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“…In total, 26 distinct combinations were identified using data of six different modalities. [188], [262], [265], [273], [274], [277], [294], [300], [376], [379], [393], Video & Audio & Sensor [252], [296], [409], Video & Audio [153], [171], [229], [253], [255], [266], [275], [281], [287], [292], [295], [298], [315], Video & Text [199], Video & Sensor [271], Video & Signal [250], [283], Image & Audio & Text [111], [175], [204], [213], [216], [263], [264], [288], [340], [396], [406], Image & Audio & Sensor & Signal [366], Image & Audio & Sensor [249], [334], [335], Image & Audio …”

Section: B Taskmentioning

confidence: 99%

“…The remaining 57 articles were related to combined models of model-based and agnostic-based models. Seven types of the combination were found: early & late [65], [78], [80], [86], [87], [109], [118], [123], [131], [135], [151], [165], [190], [206], [234], [235], [239], [251], [255], [269], [278], [281], [297], [302], [304], [314], [350], [358], [361], [363], [403], [412], [414], early & late & hybrid [74], [117], [232], [260], [263], [284], [331], [354], [370], early & late & kernel [150], [238], early & late & neural networks [60], [67], [183], [288], [409], early & neural networks [81], [226], [375], late & hybrid [309]<...…”

Section: F Fusionmentioning

confidence: 99%

“…Late fusion is also known as decision fusion. Hybrid fusion integrates outputs of early fusion and individual unimodal predictors, for example, [74], [117], [263], [331]. Multiple kernel learning, graphical models, and neural networks are subtypes of model-based fusion.…”

Section: B Inspection Of Rq2mentioning

confidence: 99%

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A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

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Section: B Taskmentioning

confidence: 99%

Section: F Fusionmentioning

confidence: 99%

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A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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“…Both Abhimanyu Sherawat et.al [2] and Aslı Gül et. al [4], went a step ahead to consider audio and body posture as components to predict personality traits apart from distinctively choosing text and body posture of the candidate respectively. Though most of the studies focused on predicting personality traits of candidates, one can observe that attempts to predict a suitable job based on the personality of the candidate has not been addressed adequately.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of Personality Traits and Suitable Job through an Intelligent Interview Agent using Machine Learning

Mandakini,

Shriya

2023

IJRASET

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Interviews play a crucial role in an individual’s career. They are often a means through which recruitments are finalized in various companies. To effectively understand the suitability of the candidate for a particular job, the interviewer not only assesses the conceptual knowledge of the candidate but also tries to identify if the personality traits of the prospect match with the job requirements. Facial expressions are crucial in human communication since they assist in understanding others better and are commonly used to assess personality. The automation ensures that the procedure for selecting candidates in an objective manner is not tainted by the interviewer's bias and personal experiences. The proposed Intelligent Interview Agent uses video input of the interviewee to predict the Big Five Personality traits as seen by skilled human resource experts. To achieve this, the system uses VGG16 Convolutional Neural Network (CNN) Model. The system also predicts the suitable job role for the candidate depending on the scores predicted for the Big Five personality traits, by employing a machine learning (ML) model. The system serves the purpose of both the recruiter and the candidate. The recruiter can analyse the candidate’s personality traits and assign him/her the predicted suitable job. On the other hand, the candidate can get an idea of his/her personality traits and know which profession suits the best.

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“…Four images, randomly chosen, are resized, cropped accordingly and stacked together. A tensor of shape (4,128,128,3) i.e. 4 video frames of 128 pixel height x 128 pixel width consisting of 3 RGB colours, is the video representation considered per sample.…”

Section: Prediction Of Scores Of the Big Five Personality Traitsmentioning

confidence: 99%

Prediction of Personality Traits and Suitable Job through an Intelligent Interview Agent using Machine Learning

Mandakini

2023

JCSA

View full text Add to dashboard Cite

Interviews play a crucial role in an individual's career. They are often a means through which recruitments are finalized in various companies. To effectively understand the suitability of the candidate for a particular job, the interviewer not only assesses the conceptual knowledge of the candidate but also tries to identify if the personality traits of the prospect match with the job requirements. Facial expressions are crucial in human communication since they assist in understanding others better and are commonly used to assess personality. The automation ensures that the procedure for selecting candidates in an objective manner is not tainted by the interviewer's bias and personal experiences. The proposed Intelligent Interview Agent uses video input of the interviewee to predict the Big Five Personality traits as seen by skilled human resource experts. To achieve this, the system uses VGG16 Convolutional Neural Network (CNN) Model. The system also predicts the suitable job role for the candidate depending on the scores predicted for the Big Five personality traits, by employing a machine learning (ML) model. The system serves the purpose of both the recruiter and the candidate. The recruiter can analyse the candidate's personality traits and assign him/her the predicted suitable job. On the other hand, the candidate can get an idea of his/her personality traits and know which profession suits the best.

show abstract

Multimodal analysis of personality traits on videos of self-presentation and induced behavior

Cited by 18 publications

References 77 publications

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

Prediction of Personality Traits and Suitable Job through an Intelligent Interview Agent using Machine Learning

Prediction of Personality Traits and Suitable Job through an Intelligent Interview Agent using Machine Learning

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